from local

egs/librispeech/ASR/conformer_ctc2/asr_metrics.py

@@ -0,0 +1,236 @@
+from __future__ import unicode_literals
+import logging
+from typing import Any, Dict, List, Tuple, Union
+import sys
+import pandas as pd
+import jiwer
+
+# -*- coding: utf-8 -*-
+
+def levenshtein(u, v):
+    prev = None
+    curr = [0] + list(range(1, len(v) + 1))
+    # Operations: (SUB, DEL, INS)
+    prev_ops = None
+    curr_ops = [(0, 0, i) for i in range(len(v) + 1)]
+    for x in range(1, len(u) + 1):
+        prev, curr = curr, [x] + ([None] * len(v))
+        prev_ops, curr_ops = curr_ops, [(0, x, 0)] + ([None] * len(v))
+        for y in range(1, len(v) + 1):
+            delcost = prev[y] + 1
+            addcost = curr[y - 1] + 1
+            subcost = prev[y - 1] + int(u[x - 1] != v[y - 1])
+            curr[y] = min(subcost, delcost, addcost)
+            if curr[y] == subcost:
+                (n_s, n_d, n_i) = prev_ops[y - 1]
+                curr_ops[y] = (n_s + int(u[x - 1] != v[y - 1]), n_d, n_i)
+            elif curr[y] == delcost:
+                (n_s, n_d, n_i) = prev_ops[y]
+                curr_ops[y] = (n_s, n_d + 1, n_i)
+            else:
+                (n_s, n_d, n_i) = curr_ops[y - 1]
+                curr_ops[y] = (n_s, n_d, n_i + 1)
+    return curr[len(v)], curr_ops[len(v)]
+
+
+
+def get_unicode_code(text):
+    result = ''.join( char if ord(char) < 128 else '\\u'+format(ord(char), 'x') for char in text )
+    return result
+
+
+def _measure_cer(
+        reference : str, transcription : str
+) -> Tuple[int, int, int, int]:
+    """
+    소스 단어를 대상 단아로 변환하는 데 필요한 편집 작업(삭제, 삽입, 바꾸기)의 수를 확인합니다.
+    hints 횟수는 소스 딘아의 전체 길이에서 삭제 및 대체 횟수를 빼서 제공할 수 있습니다.
+
+    :param transcription: 대상 단어로 변환할 소스 문자열
+    :param reference: 소스 단어
+    :return: a tuple of #hits, #substitutions, #deletions, #insertions
+    """
+
+    ref, hyp = [], []
+
+    ref.append(reference)
+    hyp.append(transcription)
+
+    #print("? : ", ref)
+
+    cer_s, cer_i, cer_d, cer_n = 0, 0, 0, 0
+    sen_err = 0
+
+    for n in range(len(ref)):
+        # update CER statistics
+        _, (s, i, d) = levenshtein(hyp[n], ref[n])
+        cer_s += s
+        cer_i += i
+        cer_d += d
+        cer_n += len(ref[n])
+
+        # update SER statistics
+        if s + i + d > 0:
+            sen_err += 1
+
+
+
+    '''
+    print("reference : ",reference)
+    print("cer S : ", cer_s)
+    print("cer I : ", cer_i)
+    print("cer D : ", cer_d)
+    print("cer_n : ", cer_n)
+
+
+    if cer_n > 0:
+        print('CER: %g%%, SER: %g%%' % (
+            (100.0 * (cer_s + cer_i + cer_d)) / cer_n,
+            (100.0 * sen_err) / len(ref)))
+    '''
+    substitutions = cer_s
+    deletions = cer_d
+    insertions = cer_i
+    hits = len(reference) - (substitutions + deletions) #correct characters
+
+    return hits, substitutions, deletions, insertions
+
+def _measure_wer(
+        reference : str, transcription : str
+) -> Tuple[int, int, int, int]:
+    """
+    소스 문자열을 대상 문자열로 변환하는 데 필요한 편집 작업(삭제, 삽입, 바꾸기)의 수를 확인합니다.
+    hints 횟수는 소스 문자열의 전체 길이에서 삭제 및 대체 횟수를 빼서 제공할 수 있습니다.
+
+    :param transcription: 대상 단어
+    :param reference: 소스 단어
+    :return: a tuple of #hits, #substitutions, #deletions, #insertions
+    """
+
+    ref, hyp = [], []
+
+    ref.append(reference)
+    hyp.append(transcription)
+
+    #print("? : ", ref)
+
+    wer_s, wer_i, wer_d, wer_n = 0, 0, 0, 0
+    sen_err = 0
+
+    for n in range(len(ref)):
+        # update WER statistics
+        _, (s, i, d) = levenshtein(hyp[n].split(), ref[n].split())
+        wer_s += s
+        wer_i += i
+        wer_d += d
+        wer_n += len(ref[n].split())
+        # update SER statistics
+        if s + i + d > 0:
+            sen_err += 1
+
+
+
+    #print("reference : ",reference)
+    #print("reference cnt : ", reference.split())
+    #print("wer S : ", wer_s)
+    #print("wer I : ", wer_i)
+    #print("wer D : ", wer_d)
+    #print("wer_n : ", wer_n)
+
+
+    if wer_n > 0:
+        print('WER: %g%%, SER: %g%%' % (
+            (100.0 * (wer_s + wer_i + wer_d)) / wer_n,
+            (100.0 * sen_err) / len(ref)))
+
+    substitutions = wer_s
+    deletions = wer_d
+    insertions = wer_i
+    hits = len(reference.split()) - (substitutions + deletions) #correct words between refs and trans
+
+    return hits, substitutions, deletions, insertions
+
+
+
+
+def _measure_er(
+        reference : str, transcription : str
+) -> Tuple[int, int]:
+    """
+    TBD
+    :param transcription: 대상 문자열로 변환할 소스 문자열
+    :param reference:
+    :return: a tuple of #
+    """
+    TBD1 =""
+    TBD2 =""
+    return TBD1, TBD2
+
+
+def get_cer(reference, transcription, rm_punctuation = True
+            ) -> Tuple[int, int, int, int]:
+
+    # 문자 오류율(CER)은 자동 음성 인식 시스템의 성능에 대한 일반적인 메트릭입니다.
+    # CER은 WER(단어 오류율)과 유사하지만 단어 대신 문자에 대해 작동합니다.
+    # 이 코드에서는 문제는 사람들이 띄어쓰기를 지키지 않고 작성한 텍스트를 컴퓨터가 정확하게 인식하는 것이 매우 어렵기 때문에 인식에러에서 생략합니다.
+    # CER의 출력은 특히 삽입 수가 많은 경우 항상 0과 1 사이의 숫자가 아닙니다. 이 값은 종종 잘못 예측된 문자의 백분율과 연관됩니다. 값이 낮을수록 좋습니다.
+    # CER이 0인 ASR 시스템의 성능은 완벽한 점수입니다.
+
+    # CER = (S + D + I) / N = (S + D + I) / (S + D + C)
+    # S is the number of the substitutions,
+    # D is the number of the deletions,
+    # I is the number of the insertions,
+    # C is the number of the correct characters,
+    # N is the number of the characters in the reference (N=S+D+C).
+
+    refs = jiwer.RemoveWhiteSpace(replace_by_space=False)(reference)
+    trans = jiwer.RemoveWhiteSpace(replace_by_space=False)(transcription)
+
+    if rm_punctuation == True:
+        refs = jiwer.RemovePunctuation()(refs)
+        trans = jiwer.RemovePunctuation()(trans)
+    else:
+        refs = reference
+        trans = transcription
+
+    #print("refs : ", refs)
+
+    [hits ,cer_s, cer_d, cer_i] = _measure_cer(refs, trans)
+
+    substitutions = cer_s
+    deletions = cer_d
+    insertions = cer_i
+    #print("tmp hits : ", hits)
+    incorrect = substitutions + deletions + insertions
+    total = substitutions + deletions + hits + insertions
+
+    cer = incorrect / total
+    return cer, substitutions, deletions, insertions
+
+
+def get_wer(reference, transcription, rm_punctuation = True
+            )-> Tuple[int, int, int, int]:
+
+    # WER = (S + D + I) / N = (S + D + I) / (S + D + C)
+    # S is the number of the substitutions,
+    # D is the number of the deletions,
+    # I is the number of the insertions,
+    # C is the number of the correct words,
+    # N is the number of the words in the reference (N=S+D+C).
+    if rm_punctuation == True:
+        refs = jiwer.RemovePunctuation()(reference)
+        trans = jiwer.RemovePunctuation()(transcription)
+    else:
+        refs = reference
+        trans = transcription
+    [hits, wer_s, wer_d, wer_i] = _measure_wer(refs, trans)
+
+    substitutions = wer_s
+    deletions = wer_d
+    insertions = wer_i
+    #print("tmp hits : ", hits)
+    incorrect = substitutions + deletions + insertions
+    total = substitutions + deletions + hits + insertions
+
+    wer = incorrect / total
+    return wer, substitutions, deletions, insertions